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Hand Motion Based Human Computer Interaction Using 3D Convolutional neural network

Hand Motion Based Human Computer Interaction Using 3D Convol...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Vachiraporn Ketsoi Muhammad Raza Haopeng Chen School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Electronic devices have achieved smart functionality due to the emergence of action recognition. More specifically, hand motions classification is one of the main research areas among computer vision research communities, which have tremendous potential in the field of human computer interaction. However, there are various challenges related to the computational cost of this approach. In this paper, a compact method is proposed to recognize hand actions by utilizing 3D convolutional neural network. Without using multiple sensors, our method is merely based on a mono camera. A network is trained using multiple videos of human hand motions, and then an image sequence is forward to the trained neural network for recognizing the corresponding action. Our trained neural network is dubbed as HMRNet, an abbreviation for hand motions recognition network. The HMRNet is trained and tested using 20BN Jester dataset, and experimental results showed that both the estimated error and the accuracy of the proposed network are superior compare to other traditional methods including sensors and features based. The computational efficiency and logical steps of our approach are straightforward that any user interface-based device equipped with a camera and proper hardware can adapt this method. This method allows users to interact with the personal computers in a very interactive and futuristic way, and has the potential to eliminate the use of input devices including mouse and touch pen.

关键词： Sensors Three-dimensional displays Feature extraction Kernel Visualization Gesture recognition Convolutional neural networks

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DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WITH INCEPTION-LIKE BLOCK 25

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

引用

25th IEEE International conference on Image processing (ICIP)

作者： Wang, Qiang Xu, Jiaqing Li, Rongchun Qiao, Peng Yang, Ke Li, Shijie Dou, Yong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China Natl Univ Def Technol Sch Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781479970612

Image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Deep Clustering Convolutional Autoencoders Kullback-Leibler divergence

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Improvement of Quantum Genetic Algorithms and Application of DDoS Attack Detection

Improvement of Quantum Genetic Algorithms and Application of...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Changqing Gong Tongyao Shi Ming Mu Liang Zhao Abdullah Gani Han Qi School of Computer Science Shenyang Aerospace University Shenyang China Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Aiming at the problem of insufficient searching ability of Quantum Genetic Algorithms (QGA), this paper proposes a method to improve QGA by dynamically changing the rotation angle of quantum revolving gates. Thirteen typical standard functions are used to test the improved QGA, and to further verify the improved QGA. In this paper, a DDoS attack detection model based on quantum genetic optimization BP neural network (DQGA-BP) is constructed. The model combines improved quantum genetic algorithm with BP neural network, and uses KDD-Cup 1999 data set (9-week network connection collected from the USAF LAN) to detect DDoS attacks. It effectively improves the accuracy of DDoS attack detection. The experimental results show that the improved quantum genetic algorithm has faster convergence speed and stronger optimization ability. Under DDoS attack detection, the average detection rate of DQGA-BP is 0.51491% higher than that of the original quantum genetic optimization BP neural network (QGA-BP), and the average false alarm rate is 0.37%.

关键词： Genetic algorithms Denial-of-service attack Biological cells Logic gates Convergence Optimization neural networks

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MIX: Multi-Channel Information Crossing for Text Matching 18

MIX: Multi-Channel Information Crossing for Text Matching

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24th ACM SIGKDD conference on Knowledge Discovery and Data Mining (KDD)

作者： Chen, Haolan Han, Fred X. Niu, Di Liu, Dong Lai, Kunfeng Wu, Chenglin Xu, Yu Tencent Mobile Internet Grp Toronto ON Canada Univ Alberta Edmonton AB Canada

ISBN: (纸本)9781450355520

Short Text Matching plays an important role in many natural language processing tasks such as information retrieval, question answering, and dialogue systems. Conventional text matching methods rely on predefined templates and rules. However, for a short piece of text with a limited number of words, these rules are unable to generalize well to unobserved data. With the success of deep learning in fields like computer vision, speech recognition and recommender systems, many recent efforts have been made to apply deep neural network models to natural language processing tasks to reduce the cost of manual feature engineering. In this paper, we present the design of Multi-Channel Information Crossing (MIX), a multi-channel convolutional neural network (CNN) model for text matching in a production environment, with additional attention mechanisms on sentences and semantic features. MIX compares text snippets at varied granularities to form a series of multi-channel similarity matrices, which are then crossed with another set of carefully designed attention matrices to expose the rich structures of sentences to deep neural networks. We implemented MIX and deployed the system on Tencent's Venus distributed computation platform. Thanks to the well-engineered multi-channel information crossing, evaluation results suggest that MIX outperforms a wide range of state-of-the-art deep neural network models by at least 11.1% in terms of the normalized discounted cumulative gain (NDCG@3), on the English WikiQA dataset. Moreover, we performed online A/B tests with real users on the search service of Tencent QQ Browser. Results show that MIX raised the number of clicks on the returned results by 5.7%, due to the increased accuracy in query-document matching, which demonstrates the superior performance of MIX in a real-world production environment.

关键词： Text matching ad-hoc retrieval question answering convolutional neural networks attention mechanism

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P-Spiking Deep neural network Based on Adaptive SPCNN Temporal Coding

P-Spiking Deep Neural Network Based on Adaptive SPCNN Tempor...

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IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Yuli Chen Huiting Yao Miao Ma Zhao Pei Xingwei Li Zengguo Sun dept.Shaanxi Normal Universi School of Computer Science Xi’an China

ISBN: (纸本)9781728152103

In this paper, we propose a P-spike deep neural network (P-SDNN) for image classification based on an adaptive simplified pulse coupled neural network (SPCNN) temporal coding. The proposed P-SDNN model introduces a SPCNN tem-coding layer into a spiking deep neural network (SDNN) with parameters adjusted by unsupervised STDP learning rule. The advantage of the proposed SPCNN temporal coding is to obtain adaptive time steps in terms of different input images. Each time step corresponds to a spiking-timing map which may contain a semantic segmentation of the input image. This is guaranteed by the working principle of SPCNN that the higher the neuron intensity is, the larger its internal activity will be and the earlier it will fire. And the adjacent neurons with similar intensity will pulse synchronously in a spiking-timing map. We evaluate the proposed P-SDNN model in the tasks of image classification on the Caltech face/motorbike and MNIST datasets. The experiments show that, under the same experimental conditions, the proposed P-SDNN model performs better than the SDNN model without SPCNN tem-coding.

关键词： image classification image segmentation neural nets unsupervised learning Image classification neural network Unsupervised learning image segmentation coding operating principles Self tuning

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Microblog search method based on neural network language model

Microblog search method based on neural network language mod...

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Chinese Intelligent Systems conference, CISC 2017

作者： Lai, Jincai Du, Junping Zheng, Wenzhen Wang, Wei Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China SINA Corporation Beijing China

ISBN: (纸本)9789811064951

Deep neural network language model has gained significant development among natural language processing (NLP) in recent years. In this paper, we focused on using neural language model (NNLM) to enhance microblog search. This paper proposed a microblog search method based on neural network language model (NBSM). Firstly, we train neural network language model based on microblog data, so as to get the distributed representation of words which may contain internal express model of microblog. Then, we use the distributed representation of words to get the expanding words of users’ searching words. Finally, we re-rank microblog search results combining deep sematic text similarity and social signal features. The method we proposed can effectively obtain microblog express model, and its search result can reflect the social hot-topics of the topic related to users searching words. Experiment results show that the proposed method yields significant improvements over state-of-arts methods and significantly improves the user’s search experience. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Citation Count Prediction Based on Academic network Features 32

Citation Count Prediction Based on Academic Network Features

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32nd IEEE International conference on Advanced Information networking and Applications (AINA)

作者： Zhu, XinPing Ban, ZhiJie Inner Mongolia Univ Sch Comp Sci Hohhot Peoples R China

ISBN: (纸本)9781538621950

Citation count is an important factor to measure the influence of academic publications. Identifying future citation count in advance can help scientists to find references and research area. There are many academic network features which are related to citation count. However, these features have not been completely explored in the existing studies. In this paper, we propose a citation count prediction model based on academic network features. Firstly, some important features are introduced and analyzed in detail. Then, we verify the importance of each feature and use a neural network model to select a set of optimal features. Finally, we present several machine learning methods and one multiple linear regression strategy to predict a paper's future citation. Experimental results on real datasets demonstrate that our model significantly outperforms the baseline method.

关键词： citation count prediction academic social network feature selection

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CNNP-v2:An Energy Efficient Memory-Centric Convolutional neural network Processor Architecture

CNNP-v2:An Energy Efficient Memory-Centric Convolutional Neu...

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IEEE International conference on Artificial Intelligence Circuits and Systems (AICAS)

作者： Sungpill Choi Kyeongryeol Bong Donghyeon Han Hoi-Jun Yoo School of Electrical Engineering KAIST Daejeon Republic of Korea

ISBN: (纸本)9781538678855

An energy efficient memory-centric convolutional neural network (CNN) processor architecture is proposed for smart devices such as wearable devices or internet of things (IoT) devices. To achieve energy-efficient processing, it has 2 key features: First, 1-D shift convolution PEs with fully distributed memory architecture achieve 3.1TOPS/W energy efficiency. Compared with conventional architecture, even though it has massively parallel 1024 MAC units, it achieve high energy efficiency by scaling down voltage to 0.46V due to its fully local routed design. Next, fully configurable 2-D mesh core-to-core interconnection support various size of input features to maximize utilization. The proposed architecture is evaluated 16mm 2 chip which is fabricated with 65nm CMOS process and it performs real-time face recognition with only 9.4mW at 10MHz and 0.48V.

关键词： Convolution Energy efficiency Memory architecture Registers Rails Hardware

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Proceedings of the 45th International conference on Very Large Data Bases, VLDB August 2019

Proceedings of the 45th International Conference on Very Lar...

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45th International conference on Very Large Data Bases, VLDB 2019

This proceedings contains 87 papers. The VLDB 2019 conference covers issues in data management, database and information systems research. VLDB 2019 covers many aspects of data management and analytics, including data integration, cloud databases, distributed transactions, query processing and optimization, crowdsourcing, graph analytics, scalable machine learning, and distributed systems. The key terms of this proceedings include synergistic graph, scalable spatial data analysis, Vehicle Image Search Engine - VISE, distributed machine learning, unified data analytics optimizer, databases and big data systems, temporal database management system, graph neural network, large-scale data processing systems, geo-distributed data. © 2019 VLDB Endowment.

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A Proposed License Plate Classification Method Based on Convolutional neural network 6

A Proposed License Plate Classification Method Based on Conv...

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6th IEEE International conference on network Infrastructure and Digital Content (IEEE IC-NIDC)

作者： Ni, Kaili Fu, Meixia Huang, Zhongjie Sun, Songlin Beijing Univ Posts & Telecommun Natl Engn Lab Mobile Network Secur Key Lab Trustworthy Distributed Comp & Serv BUPT Sch Informat & Commun EngnMinist Educ Beijing Peoples R China

ISBN: (纸本)9781538660676

Nowadays convolutional neural network(CNN) has been successfully applied in image processing. At the same tune license plate recognition is more and more universal. Before recognition with deep learning, we need to collect enough images to train the network. The quality of data is very important. The current methods of getting license plate based on deep learning are increasingly various, however, there are still many images where illumination, size and blurriness make it is extremely difficult to recognize. As a result, images with low quality eventually affect the accuracy of recognition. Therefore, license plate classification is essential to eliminate low quality images so that improve the quality of the dataset. In this paper, a method based on CNN is proposed to deal with license plate classification. We use a seven layers CNN and ultimately the best result reached 98.79%.

关键词： CNN License plate Classification Recognition

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